Prediction of the Dollar Exchange Rate Against the Rupiah Based on Indonesian Economic Growth using the Long Shortterm Memory (LSTM) Method

This study aims to predict the Dollar-Rupiah exchange rate based on Indonesia's economic growth using the Long Short-Term Memory (LSTM) method. Exchange rate fluctuations that occurred from 1997 to 2024 have had a significant impact on national economic stability, so a predictive model capable of accurately interpreting historical patterns is needed. Research data was obtained from the Central Bureau of Statistics (BPS), Bank Indonesia (BI), and global economic sources with variables used including the Dollar-Rupiah exchange rate, Gross Domestic Product (GDP), inflation, and interest rates. The research stages include data preprocessing, LSTM architecture development, training, and testing using Google Colab. The results show that the LSTM model is able to produce exchange rate predictions that are close to actual data with a relatively low error rate. Thus, the LSTM method can be used as an effective approach to assisting in the analysis of exchange rate movements and supporting economic policymaking in Indonesia.